@inproceedings{chandakacherla-etal-2024-uic,
    title = "{UIC} {NLP} {GRADS} at {S}em{E}val-2024 Task 3: Two-Step Disjoint Modeling for Emotion-Cause Pair Extraction",
    author = "Chandakacherla, Sharad  and
      Bhargava, Vaibhav  and
      Parde, Natalie",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.semeval-1.198/",
    doi = "10.18653/v1/2024.semeval-1.198",
    pages = "1373--1379",
    abstract = "Disentangling underlying factors contributing to the expression of emotion in multimodal data is challenging but may accelerate progress toward many real-world applications. In this paper we describe our approach for solving SemEval-2024 Task {\#}3, Sub-Task {\#}1, focused on identifying utterance-level emotions and their causes using the text available from the multimodal F.R.I.E.N.D.S. television series dataset. We propose to disjointly model emotion detection and causal span detection, borrowing a paradigm popular in question answering (QA) to train our model. Through our experiments we find that (a) contextual utterances before and after the target utterance play a crucial role in emotion classification; and (b) once the emotion is established, detecting the causal spans resulting in that emotion using our QA-based technique yields promising results."
}